Sentence examples for all classifiers used from inspiring English sources

All classifiers used 10 features.

The highest classification accuracies are obtained with the hybrid filter wrapper genetic algorithm, for all classifiers used in this paper.

Zhang [2] proposed an Optimal Weight values for classifiers in the case of abrupt concept drift, in this algorithm all classifiers using different learning algorithms, e.g., Decision Tree, SVM, LR, and then builds prediction models on and only on the up-to-data data chunk.

All classifiers use the Local Alignment Kernel and tests are repeated ten times with different data set divisions.

For the 7TMRmine server, we updated all classifiers using a larger training dataset, and added new classifiers (SAM1, SAM2, GPCRHMM, and Phobius).

As shown in Figure 3, the SVM classifier with PSSM + is slightly better than the classifier with PSSM, and all the three classifiers using evolutionary information are clearly better than the SVM trained with only the biochemical features (HKM).

Physicochemical classifiers used as compound descriptors.

These are the machine learning classifiers used for sentiment analysis.

This parameter determines the overall quality of the classifiers used.

The base classifiers used were Naive Bayes and Random forest.

Section 5 describes the classifiers used for person verification systems.